We run this script under TensorFlow 2.0 and the TensorLayer 2.0+. For TensorLayer 1.4 version, please check release.
ππππππ THIS PROJECT WILL BE CLOSED AND MOVED TO THIS FOLDER IN NEXT MONTH.
ππππππ THIS PROJECT WILL BE CLOSED AND MOVED TO THIS FOLDER IN NEXT MONTH.
ππππππ THIS PROJECT WILL BE CLOSED AND MOVED TO THIS FOLDER IN NEXT MONTH.
TensorFlow Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
![](img/model.jpeg)
![](img/SRGAN_Result2.png)
![](img/SRGAN_Result3.png)
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- You need to download the pretrained VGG19 model in here as tutorial_vgg19.py show.
-
- You need to have the high resolution images for training.
- In this experiment, I used images from DIV2K - bicubic downscaling x4 competition, so the hyper-paremeters in
config.py
(like number of epochs) are seleted basic on that dataset, if you change a larger dataset you can reduce the number of epochs. - If you dont want to use DIV2K dataset, you can also use Yahoo MirFlickr25k, just simply download it using
train_hr_imgs = tl.files.load_flickr25k_dataset(tag=None)
inmain.py
. - If you want to use your own images, you can set the path to your image folder via
config.TRAIN.hr_img_path
inconfig.py
.
- Set your image folder in
config.py
, if you download DIV2K - bicubic downscaling x4 competition dataset, you don't need to change it. - Other links for DIV2K, in case you can't find it : test_LR_bicubic_X4, train_HR, train_LR_bicubic_X4, valid_HR, valid_LR_bicubic_X4.
config.TRAIN.img_path = "your_image_folder/"
- Start training.
python train.py
- Start evaluation.
python train.py --mode=evaluate
- [1] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- [2] Is the deconvolution layer the same as a convolutional layer ?
If you find this project useful, we would be grateful if you cite the TensorLayer paperοΌ
@article{tensorlayer2017,
author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
journal = {ACM Multimedia},
title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
url = {http://tensorlayer.org},
year = {2017}
}
- For academic and non-commercial use only.
- For commercial use, please contact [email protected].